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To achieve high quality of silicon ICs, system-level test (SLT) can be performed after regular final test. This is important for chips manufactured in advanced technologies, as systematic failures are getting harder to detect by conventional structural tests. However, due to long test time and extra human efforts, the cost for SLT is high. A possible way to replace SLT without quality loss is to identify...
A new computational scheme for visual attention modeling is proposed. It adopts both low-level and high-level features to predict visual attention from a video signal and fuses the features by using machine learning. We show that such a scheme is more robust than those using purely single level features. Unlike conventional techniques, our scheme is able to avoid perceptual mismatch between the estimated...
Clustering for better representation of the diversity of text or image search results has been studied extensively. In this paper, we extend this methodology to the novel domain of music search. We conduct empirical evaluation of different clustering algorithms, audio feature representations, and the incorporation of lyrics for music clustering. Our evaluation shows the fusion of audio and text features...
VLSI implementation of probabilistic models is attractive for many biomedical applications. However, hardware non-idealities can prevent probabilistic VLSI models from modelling data optimally through on-chip learning. This paper investigates the maximum computational errors that a probabilistic VLSI model can tolerate when modelling real biomedical data. VLSI circuits capable of achieving the required...
VLSI implementation of probabilistic models is attractive for many biomedical applications. However, hardware non-idealities can prevent probabilistic VLSI models from modelling data optimally through on-chip learning. This paper investigates the maximum computational errors that a probabilistic VLSI model can tolerate when modelling real biomedical data. VLSI circuits capable of achieving the required...
The authors review briefly the procedure relating to the convergence analysis of a learning algorithm for adaptive feature extraction. They then address the issue of identification of a nontrivial domain of attraction for the learning system. The problem is important because such an identification is not only powerful for choosing initial settings of the system, but also holds one of the keys to the...
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